基于机器学习的研究,研究了末期病患者免疫状况和左心室缩之间的关系
Min Yang1,2, Bo Peng1,2, Quan Zhuang1,2
1Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, China.
Frontiers in cardiovascular medicine
|June 5, 2023
概括
末期病 (ESKD) 和左心室缩 (LVH) 患者表现出免疫功能受损. 机器学习模型,特别是随机森林,可以预测ESKD患者的LVH风险,建议免疫调节作为潜在的治疗方法.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 免疫学 免疫学 免疫学
背景情况:
- 左心室缩 (LVH) 是末期病 (ESKD) 患者常见的心脏并发症,预测结果不佳.
- 免疫系统在心脏缩发展中的作用越来越多地在各种疾病中得到认可.
- 研究ESKD患者的免疫状况对于了解LVH病变发生至关重要.
研究的目的:
- 在末期病 (ESKD) 患者中探索免疫状况和左心室缩 (LVH) 之间的关系.
- 开发和评估用于预测ESKD中的LVH的机器学习模型.
- 为了确定与LVH发生和严重程度相关的特定免疫参数.
主要方法:
- 506名ESKD患者的回顾性分析,同时进行免疫状况评估和心声回声.
- 单变量和多变量逻辑回归用于评估免疫参数和LVH之间的关联.
- 机器学习模型包括支持矢量机器 (SVM),物流回归 (LR),多层感知器 (MLP) 和随机森林 (RF) 被用于预测.
主要成果:
- 与非LVH组相比,患有LVH的ESKD患者的CD3+T细胞,CD4+T细胞,CD8+T细胞和B细胞数量显著降低.
- 减少的CD3+T细胞计数是LVH的独立风险因素,而减少的NK细胞计数与LVH严重程度相关.
- 随机森林 (RF) 模型实现了高预测性能,曲线下的面积 (AUC) 为0.942.
结论:
- 在ESKD患者中,免疫参数和LVH之间存在显著的关联.
- 机器学习,特别是RF模型,显示出强大的预测能力,用于识别患有LVH风险的ESKD患者.
- 免疫调节成为一种潜在的治疗策略,用于预防和治疗ESKD患者群体中的LVH.
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